{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "fileurl = 'job_chance.csv'\n",
    "df_job02 = pandas.read_csv(fileurl, encoding=\"utf-8\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_job03 = pandas.read_csv(fileurl, encoding=\"utf-8\", index_col=0, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df_job03.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gzmt = pandas.read_csv('600519.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4094, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gzmt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Person:\n",
    "\n",
    "      # 构造函数\n",
    "      def __init__(self, name):\n",
    "            self.name = name\n",
    "\n",
    "      # 定义方法\n",
    "      def whoami(self):\n",
    "           return \"You are \" + self.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4094 entries, 0 to 4093\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   day         4094 non-null   object \n",
      " 1   STOCK_CODE  4094 non-null   int64  \n",
      " 2   open        4094 non-null   float64\n",
      " 3   close       4094 non-null   float64\n",
      " 4   maximum     4094 non-null   float64\n",
      " 5   minimum     4094 non-null   float64\n",
      " 6   volume      4094 non-null   int64  \n",
      " 7   TURNOVER    4094 non-null   int64  \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 256.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_gzmt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6005191, 3002342])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gzmt[\"STOCK_CODE\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STOCK_CODE</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>maximum</th>\n",
       "      <th>minimum</th>\n",
       "      <th>volume</th>\n",
       "      <th>TURNOVER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.095000e+03</td>\n",
       "      <td>4095.000000</td>\n",
       "      <td>4095.000000</td>\n",
       "      <td>4095.000000</td>\n",
       "      <td>4095.000000</td>\n",
       "      <td>4095.000000</td>\n",
       "      <td>4.095000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.004458e+06</td>\n",
       "      <td>182.125853</td>\n",
       "      <td>182.399360</td>\n",
       "      <td>184.843758</td>\n",
       "      <td>179.765468</td>\n",
       "      <td>27185.132845</td>\n",
       "      <td>6.489845e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.692524e+04</td>\n",
       "      <td>165.853675</td>\n",
       "      <td>165.990502</td>\n",
       "      <td>168.077290</td>\n",
       "      <td>163.748167</td>\n",
       "      <td>25323.629193</td>\n",
       "      <td>9.549837e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.002342e+06</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>20.880000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>20.710000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>1.421413e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.005191e+06</td>\n",
       "      <td>47.400000</td>\n",
       "      <td>47.480000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>10190.500000</td>\n",
       "      <td>6.206575e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.005191e+06</td>\n",
       "      <td>159.890000</td>\n",
       "      <td>159.980000</td>\n",
       "      <td>161.910000</td>\n",
       "      <td>157.870000</td>\n",
       "      <td>22497.000000</td>\n",
       "      <td>3.728447e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.005191e+06</td>\n",
       "      <td>208.500000</td>\n",
       "      <td>209.445000</td>\n",
       "      <td>212.150000</td>\n",
       "      <td>205.890000</td>\n",
       "      <td>36790.500000</td>\n",
       "      <td>7.491353e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.005191e+06</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>799.190000</td>\n",
       "      <td>803.500000</td>\n",
       "      <td>788.880000</td>\n",
       "      <td>406318.000000</td>\n",
       "      <td>1.066339e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         STOCK_CODE         open        close      maximum      minimum  \\\n",
       "count  4.095000e+03  4095.000000  4095.000000  4095.000000  4095.000000   \n",
       "mean   6.004458e+06   182.125853   182.399360   184.843758   179.765468   \n",
       "std    4.692524e+04   165.853675   165.990502   168.077290   163.748167   \n",
       "min    3.002342e+06    20.900000    20.880000    21.000000    20.710000   \n",
       "25%    6.005191e+06    47.400000    47.480000    48.200000    46.700000   \n",
       "50%    6.005191e+06   159.890000   159.980000   161.910000   157.870000   \n",
       "75%    6.005191e+06   208.500000   209.445000   212.150000   205.890000   \n",
       "max    6.005191e+06   800.000000   799.190000   803.500000   788.880000   \n",
       "\n",
       "              volume      TURNOVER  \n",
       "count    4095.000000  4.095000e+03  \n",
       "mean    27185.132845  6.489845e+08  \n",
       "std     25323.629193  9.549837e+08  \n",
       "min       238.000000  1.421413e+06  \n",
       "25%     10190.500000  6.206575e+07  \n",
       "50%     22497.000000  3.728447e+08  \n",
       "75%     36790.500000  7.491353e+08  \n",
       "max    406318.000000  1.066339e+10  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gzmt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('volume', 'TURNOVER')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('volume', 'TURNOVER')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/fn/dl1ymnx51px3qp4kjqz5rw3m0000gn/T/ipykernel_2815/1218105196.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_gzmt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'volume'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'TURNOVER'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('volume', 'TURNOVER')"
     ]
    }
   ],
   "source": [
    "df_gzmt['volume'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gzmt[\"place\"] = df_gzmt[\"STOCK_CODE\"] % 10\n",
    "# 提取STOCK_CODE的前6位，表示股票上市代号\n",
    "df_gzmt[\"code\"] = df_gzmt[\"STOCK_CODE\"] // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8 % 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8 // 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>STOCK_CODE</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>maximum</th>\n",
       "      <th>minimum</th>\n",
       "      <th>volume</th>\n",
       "      <th>TURNOVER</th>\n",
       "      <th>place</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>6005191</td>\n",
       "      <td>752.21</td>\n",
       "      <td>782.52</td>\n",
       "      <td>783.00</td>\n",
       "      <td>752.21</td>\n",
       "      <td>64592</td>\n",
       "      <td>5001164544</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3898</th>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>6005191</td>\n",
       "      <td>785.00</td>\n",
       "      <td>785.71</td>\n",
       "      <td>788.88</td>\n",
       "      <td>773.48</td>\n",
       "      <td>47714</td>\n",
       "      <td>3731993152</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3900</th>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>6005191</td>\n",
       "      <td>773.77</td>\n",
       "      <td>788.42</td>\n",
       "      <td>788.80</td>\n",
       "      <td>767.02</td>\n",
       "      <td>45988</td>\n",
       "      <td>3577281776</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901</th>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>6005191</td>\n",
       "      <td>793.46</td>\n",
       "      <td>785.37</td>\n",
       "      <td>799.06</td>\n",
       "      <td>779.02</td>\n",
       "      <td>52473</td>\n",
       "      <td>4136645488</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>6005191</td>\n",
       "      <td>752.35</td>\n",
       "      <td>781.97</td>\n",
       "      <td>782.90</td>\n",
       "      <td>745.88</td>\n",
       "      <td>73806</td>\n",
       "      <td>5697540352</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>6005191</td>\n",
       "      <td>786.50</td>\n",
       "      <td>788.05</td>\n",
       "      <td>794.70</td>\n",
       "      <td>777.23</td>\n",
       "      <td>52630</td>\n",
       "      <td>4136578576</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>6005191</td>\n",
       "      <td>788.00</td>\n",
       "      <td>785.75</td>\n",
       "      <td>800.95</td>\n",
       "      <td>782.30</td>\n",
       "      <td>48969</td>\n",
       "      <td>3870616720</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>6005191</td>\n",
       "      <td>789.98</td>\n",
       "      <td>780.97</td>\n",
       "      <td>795.55</td>\n",
       "      <td>778.90</td>\n",
       "      <td>39168</td>\n",
       "      <td>3078137472</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>2018-06-12</td>\n",
       "      <td>6005191</td>\n",
       "      <td>778.00</td>\n",
       "      <td>799.19</td>\n",
       "      <td>803.50</td>\n",
       "      <td>776.50</td>\n",
       "      <td>55287</td>\n",
       "      <td>4385043200</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>6005191</td>\n",
       "      <td>800.00</td>\n",
       "      <td>790.33</td>\n",
       "      <td>802.62</td>\n",
       "      <td>788.88</td>\n",
       "      <td>35244</td>\n",
       "      <td>2803441680</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000</th>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>6005191</td>\n",
       "      <td>790.00</td>\n",
       "      <td>786.13</td>\n",
       "      <td>793.80</td>\n",
       "      <td>775.18</td>\n",
       "      <td>36160</td>\n",
       "      <td>2830514224</td>\n",
       "      <td>1</td>\n",
       "      <td>600519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             day  STOCK_CODE    open   close  maximum  minimum  volume  \\\n",
       "3897  2018-01-09     6005191  752.21  782.52   783.00   752.21   64592   \n",
       "3898  2018-01-10     6005191  785.00  785.71   788.88   773.48   47714   \n",
       "3900  2018-01-12     6005191  773.77  788.42   788.80   767.02   45988   \n",
       "3901  2018-01-15     6005191  793.46  785.37   799.06   779.02   52473   \n",
       "3992  2018-06-04     6005191  752.35  781.97   782.90   745.88   73806   \n",
       "3993  2018-06-05     6005191  786.50  788.05   794.70   777.23   52630   \n",
       "3994  2018-06-06     6005191  788.00  785.75   800.95   782.30   48969   \n",
       "3995  2018-06-07     6005191  789.98  780.97   795.55   778.90   39168   \n",
       "3998  2018-06-12     6005191  778.00  799.19   803.50   776.50   55287   \n",
       "3999  2018-06-13     6005191  800.00  790.33   802.62   788.88   35244   \n",
       "4000  2018-06-14     6005191  790.00  786.13   793.80   775.18   36160   \n",
       "\n",
       "        TURNOVER  place    code  \n",
       "3897  5001164544      1  600519  \n",
       "3898  3731993152      1  600519  \n",
       "3900  3577281776      1  600519  \n",
       "3901  4136645488      1  600519  \n",
       "3992  5697540352      1  600519  \n",
       "3993  4136578576      1  600519  \n",
       "3994  3870616720      1  600519  \n",
       "3995  3078137472      1  600519  \n",
       "3998  4385043200      1  600519  \n",
       "3999  2803441680      1  600519  \n",
       "4000  2830514224      1  600519  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gzmt[df_gzmt[\"close\"]>=780]"
   ]
  }
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